DigiEdze AI · GPU & AI Infrastructure
Innovate, Secure, and Build AI Infra Economically Efficient
The build-versus-rent decision for AI compute is an economics question with a sovereignty dimension. We size it honestly, design for utilisation rather than peak, and build the scheduling that keeps expensive silicon busy.
- GPU clusters
- GPU scheduling
- inference
- model serving
- Kubernetes
- GPU optimization
- private AI
Reference architecture
- 06Capacity plan — owned · reserved · burst
- 05Kubernetes GPU clusters
- 04Scheduler — queues · quotas · fractional GPU
- 03Serving runtimes with batching & quantisation
- 02Model & adapter routing
- 01Utilisation and cost telemetry
The problem
What Changes When This Goes Wrong?
GPU capacity is bought for peak and sits idle most of the month.
Teams cannot get capacity when they need it, so they buy their own and fragment the estate.
Serving configuration is left at defaults, wasting a large share of available throughput.
What we build
GPU and AI Infrastructure Capability Grid
Capacity strategy
Owned, reserved and burst capacity modelled against real utilisation curves rather than vendor sizing guides.
GPU cluster engineering
Kubernetes-based clusters with device plugins, topology-aware scheduling, health checking and node lifecycle management.
Scheduling and sharing
Queues, quotas, fractional GPU allocation and pre-emption so training and inference coexist without starving each other.
Inference optimization
Batching, quantisation, KV-cache tuning and serving-runtime selection to raise tokens per second per device.
Model serving platform
Multi-model serving with autoscaling, adapter routing and cost-aware placement.
Private inference
In-perimeter serving for workloads that cannot leave the boundary.
How it works
We build the deepest and most demanding enterprise ML capabilities by integrating and embedding the industry’s top-notch platforms and our own intelligence capability set.
Technology
- NVIDIA GPU platforms
- Kubernetes
- Device plugins and schedulers
- Inference servers
- Quantisation toolchains
- dZGaaS
Use Cases
AI and GPU Tailored Across Verticals
Government
Sovereign GPU capacity operated in-country with no external inference dependency.
BFSI
Private inference for workloads that cannot cross the enterprise boundary.
Technology
Shared cluster with fair-share scheduling replacing per-team GPU purchases.
Outcomes
What We Measure
Enable the full potential of AI services by enabling the powerful infrastructure that brings cultural innovation for now and the future.
- GPU utilisation rate
- Tokens per second per device
- Cost per million tokens served
- Capacity wait time
Questions
GPU & AI Infrastructure, Answered Directly
Should we buy GPUs or rent them?
It depends on sustained utilisation and residency requirements. Below roughly half utilisation, rented capacity usually wins on total cost; above it, ownership starts to pay back — and sovereignty requirements can decide the question regardless of the arithmetic. We model your actual curve rather than applying a rule of thumb.
What most improves inference economics?
Utilisation, then serving configuration. Continuous batching and appropriate quantisation typically deliver a large multiple on throughput per device before any additional hardware is purchased.
Choose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Design Your AI Infrastructure
A paradigm shift for enterprises where modernized AI powers your future through the frameworks we bring and platforms we embed into your infrastructure.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT